Overview

Dataset statistics

Number of variables10
Number of observations741
Missing cells2026
Missing cells (%)27.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.0 KiB
Average record size in memory80.2 B

Variable types

Categorical1
Numeric9

Alerts

ne_imp_gnfs_kn is highly correlated with bx_gsr_gnfs_cd and 1 other fieldsHigh correlation
bx_gsr_gnfs_cd is highly correlated with ne_imp_gnfs_kn and 2 other fieldsHigh correlation
ny_gdp_mktp_cd is highly correlated with ne_imp_gnfs_kn and 2 other fieldsHigh correlation
sp_ado_tfrt is highly correlated with sp_dyn_le00_fe_in and 1 other fieldsHigh correlation
sl_tlf_0714_zs is highly correlated with bx_gsr_gnfs_cd and 1 other fieldsHigh correlation
sp_dyn_le00_fe_in is highly correlated with sp_ado_tfrt and 1 other fieldsHigh correlation
sp_dyn_le00_ma_in is highly correlated with sp_ado_tfrt and 1 other fieldsHigh correlation
bx_gsr_gnfs_cd is highly correlated with ny_gdp_mktp_cdHigh correlation
ny_gdp_mktp_cd is highly correlated with bx_gsr_gnfs_cdHigh correlation
sp_ado_tfrt is highly correlated with sp_dyn_le00_fe_in and 1 other fieldsHigh correlation
sp_dyn_le00_fe_in is highly correlated with sp_ado_tfrt and 1 other fieldsHigh correlation
sp_dyn_le00_ma_in is highly correlated with sp_ado_tfrt and 1 other fieldsHigh correlation
ne_imp_gnfs_kn is highly correlated with ny_gdp_mktp_cdHigh correlation
bx_gsr_gnfs_cd is highly correlated with ny_gdp_mktp_cdHigh correlation
ny_gdp_mktp_cd is highly correlated with ne_imp_gnfs_kn and 1 other fieldsHigh correlation
sp_dyn_le00_fe_in is highly correlated with sp_dyn_le00_ma_inHigh correlation
sp_dyn_le00_ma_in is highly correlated with sp_dyn_le00_fe_inHigh correlation
economy is highly correlated with si_pov_gini and 7 other fieldsHigh correlation
time is highly correlated with sl_tlf_0714_zsHigh correlation
si_pov_gini is highly correlated with economy and 3 other fieldsHigh correlation
ne_imp_gnfs_kn is highly correlated with economyHigh correlation
bx_gsr_gnfs_cd is highly correlated with economy and 4 other fieldsHigh correlation
ny_gdp_mktp_cd is highly correlated with economy and 1 other fieldsHigh correlation
sp_ado_tfrt is highly correlated with economy and 4 other fieldsHigh correlation
sl_tlf_0714_zs is highly correlated with economy and 1 other fieldsHigh correlation
sp_dyn_le00_fe_in is highly correlated with economy and 4 other fieldsHigh correlation
sp_dyn_le00_ma_in is highly correlated with economy and 4 other fieldsHigh correlation
si_pov_gini has 613 (82.7%) missing values Missing
ne_imp_gnfs_kn has 249 (33.6%) missing values Missing
bx_gsr_gnfs_cd has 135 (18.2%) missing values Missing
ny_gdp_mktp_cd has 43 (5.8%) missing values Missing
sp_ado_tfrt has 95 (12.8%) missing values Missing
sl_tlf_0714_zs has 707 (95.4%) missing values Missing
sp_dyn_le00_fe_in has 92 (12.4%) missing values Missing
sp_dyn_le00_ma_in has 92 (12.4%) missing values Missing
economy is uniformly distributed Uniform

Reproduction

Analysis started2022-01-23 16:30:36.587832
Analysis finished2022-01-23 16:30:47.482583
Duration10.89 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

economy
Categorical

HIGH CORRELATION
UNIFORM

Distinct39
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
ASM
 
19
PYF
 
19
MYS
 
19
NCL
 
19
NPL
 
19
Other values (34)
646 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASM
2nd rowASM
3rd rowASM
4th rowASM
5th rowASM

Common Values

ValueCountFrequency (%)
ASM19
 
2.6%
PYF19
 
2.6%
MYS19
 
2.6%
NCL19
 
2.6%
NPL19
 
2.6%
PHL19
 
2.6%
PLW19
 
2.6%
PNG19
 
2.6%
PRK19
 
2.6%
SGP19
 
2.6%
Other values (29)551
74.4%

Length

2022-01-23T17:30:47.532485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
asm19
 
2.6%
ind19
 
2.6%
brn19
 
2.6%
btn19
 
2.6%
chn19
 
2.6%
fji19
 
2.6%
fsm19
 
2.6%
gum19
 
2.6%
hkg19
 
2.6%
mdv19
 
2.6%
Other values (29)551
74.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

time
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009
Minimum2000
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-23T17:30:47.621213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2009
Q32014
95-th percentile2018
Maximum2018
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.480925154
Coefficient of variation (CV)0.002728185741
Kurtosis-1.20670747
Mean2009
Median Absolute Deviation (MAD)5
Skewness0
Sum1488669
Variance30.04054054
MonotonicityNot monotonic
2022-01-23T17:30:47.712478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
200039
 
5.3%
201039
 
5.3%
201739
 
5.3%
201639
 
5.3%
201539
 
5.3%
201439
 
5.3%
201339
 
5.3%
201239
 
5.3%
201139
 
5.3%
200939
 
5.3%
Other values (9)351
47.4%
ValueCountFrequency (%)
200039
5.3%
200139
5.3%
200239
5.3%
200339
5.3%
200439
5.3%
200539
5.3%
200639
5.3%
200739
5.3%
200839
5.3%
200939
5.3%
ValueCountFrequency (%)
201839
5.3%
201739
5.3%
201639
5.3%
201539
5.3%
201439
5.3%
201339
5.3%
201239
5.3%
201139
5.3%
201039
5.3%
200939
5.3%

si_pov_gini
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct94
Distinct (%)73.4%
Missing613
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean37.7125
Minimum27.8
Maximum47.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-23T17:30:47.833213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27.8
5-th percentile31.335
Q135.025
median37.75
Q340.325
95-th percentile45.89
Maximum47.7
Range19.9
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation4.36527096
Coefficient of variation (CV)0.1157513015
Kurtosis-0.3792954981
Mean37.7125
Median Absolute Deviation (MAD)2.65
Skewness0.09461920787
Sum4827.2
Variance19.05559055
MonotonicityNot monotonic
2022-01-23T17:30:47.959149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.75
 
0.7%
38.14
 
0.5%
35.73
 
0.4%
39.33
 
0.4%
373
 
0.4%
32.32
 
0.3%
36.42
 
0.3%
39.42
 
0.3%
37.82
 
0.3%
35.42
 
0.3%
Other values (84)100
 
13.5%
(Missing)613
82.7%
ValueCountFrequency (%)
27.81
0.1%
28.61
0.1%
28.71
0.1%
291
0.1%
30.71
0.1%
31.21
0.1%
31.31
0.1%
31.41
0.1%
31.61
0.1%
31.72
0.3%
ValueCountFrequency (%)
47.71
0.1%
47.21
0.1%
46.61
0.1%
46.51
0.1%
46.41
0.1%
46.31
0.1%
46.11
0.1%
45.51
0.1%
44.81
0.1%
44.61
0.1%

ne_imp_gnfs_kn
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct491
Distinct (%)99.8%
Missing249
Missing (%)33.6%
Infinite0
Infinite (%)0.0%
Mean1.52152412 × 1014
Minimum-6.2044893 × 1010
Maximum4.696315 × 1015
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size5.9 KiB
2022-01-23T17:30:48.090802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-6.2044893 × 1010
5-th percentile198602500
Q13755466700
median5.2195769 × 1011
Q37.54778135 × 1012
95-th percentile1.228832715 × 1015
Maximum4.696315 × 1015
Range4.696377045 × 1015
Interquartile range (IQR)7.544025883 × 1012

Descriptive statistics

Standard deviation5.227785841 × 1014
Coefficient of variation (CV)3.43588759
Kurtosis26.99295494
Mean1.52152412 × 1014
Median Absolute Deviation (MAD)5.215141894 × 1011
Skewness4.747739754
Sum7.485898669 × 1016
Variance2.73297448 × 1029
MonotonicityNot monotonic
2022-01-23T17:30:48.208488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6680000002
 
0.3%
8.1457086 × 10111
 
0.1%
4.5774573 × 10111
 
0.1%
4.4423224 × 10111
 
0.1%
4.6606424 × 10111
 
0.1%
3.6338083 × 10111
 
0.1%
3.2262498 × 10111
 
0.1%
2.9819706 × 10111
 
0.1%
2.896644 × 10111
 
0.1%
2.7209448 × 10111
 
0.1%
Other values (481)481
64.9%
(Missing)249
33.6%
ValueCountFrequency (%)
-6.2044893 × 10101
0.1%
1072030001
0.1%
1167310001
0.1%
1204510001
0.1%
1206090001
0.1%
1336204961
0.1%
1357860001
0.1%
1394647001
0.1%
1416707001
0.1%
1451966101
0.1%
ValueCountFrequency (%)
4.696315 × 10151
0.1%
4.163134 × 10151
0.1%
3.543103 × 10151
0.1%
3.073334 × 10151
0.1%
2.6019672 × 10151
0.1%
2.3066133 × 10151
0.1%
2.2032699 × 10151
0.1%
1.9871139 × 10151
0.1%
1.9656771 × 10151
0.1%
1.9648192 × 10151
0.1%

bx_gsr_gnfs_cd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct606
Distinct (%)100.0%
Missing135
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean1.218970969 × 1011
Minimum1274564.4
Maximum2.6510096 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-23T17:30:48.404964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1274564.4
5-th percentile27674469.5
Q1545300782.5
median4912909350
Q38.168208125 × 1010
95-th percentile6.00048105 × 1011
Maximum2.6510096 × 1012
Range2.651008325 × 1012
Interquartile range (IQR)8.113678047 × 1010

Descriptive statistics

Standard deviation3.228903774 × 1011
Coefficient of variation (CV)2.648876681
Kurtosis29.80134759
Mean1.218970969 × 1011
Median Absolute Deviation (MAD)4843201055
Skewness5.008029469
Sum7.386964072 × 1013
Variance1.042581958 × 1023
MonotonicityNot monotonic
2022-01-23T17:30:48.529406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.664591 × 10101
 
0.1%
827065101
 
0.1%
801611301
 
0.1%
876924101
 
0.1%
995723601
 
0.1%
925332401
 
0.1%
956641361
 
0.1%
1147355601
 
0.1%
1316320161
 
0.1%
1392282101
 
0.1%
Other values (596)596
80.4%
(Missing)135
 
18.2%
ValueCountFrequency (%)
1274564.41
0.1%
1635078.91
0.1%
2015177.81
0.1%
26270191
0.1%
27159791
0.1%
3015215.21
0.1%
3266386.21
0.1%
4060750.81
0.1%
5513224.51
0.1%
5715816.51
0.1%
ValueCountFrequency (%)
2.6510096 × 10121
0.1%
2.462902 × 10121
0.1%
2.4292774 × 10121
0.1%
2.3601524 × 10121
0.1%
2.3555948 × 10121
0.1%
2.1979225 × 10121
0.1%
2.175092 × 10121
0.1%
2.0088525 × 10121
0.1%
1.6564117 × 10121
0.1%
1.4978688 × 10121
0.1%

ny_gdp_mktp_cd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct698
Distinct (%)100.0%
Missing43
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean3.494643272 × 1011
Minimum13196545
Maximum1.3894818 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-23T17:30:48.648089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13196545
5-th percentile141444830
Q1676880960
median7160627700
Q31.41535435 × 1011
95-th percentile1.48668721 × 1012
Maximum1.3894818 × 1013
Range1.38948048 × 1013
Interquartile range (IQR)1.40858554 × 1011

Descriptive statistics

Standard deviation1.332833329 × 1012
Coefficient of variation (CV)3.813932426
Kurtosis49.50097942
Mean3.494643272 × 1011
Median Absolute Deviation (MAD)6976300350
Skewness6.62556601
Sum2.439261004 × 1014
Variance1.776444684 × 1024
MonotonicityNot monotonic
2022-01-23T17:30:48.766372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3111525 × 10101
 
0.1%
1.6002656 × 10101
 
0.1%
2.162171 × 10101
 
0.1%
2.1703107 × 10101
 
0.1%
2.2162209 × 10101
 
0.1%
2.2731604 × 10101
 
0.1%
2.4360796 × 10101
 
0.1%
2.4524098 × 10101
 
0.1%
2.897159 × 10101
 
0.1%
8.36697 × 10101
 
0.1%
Other values (688)688
92.8%
(Missing)43
 
5.8%
ValueCountFrequency (%)
131965451
0.1%
137420571
0.1%
154509941
0.1%
182310781
0.1%
215349321
0.1%
218390981
0.1%
229028621
0.1%
270303741
0.1%
271010761
0.1%
302902201
0.1%
ValueCountFrequency (%)
1.3894818 × 10131
0.1%
1.2310409 × 10131
0.1%
1.1233276 × 10131
0.1%
1.1061553 × 10131
0.1%
1.0475683 × 10131
0.1%
9.570407 × 10121
0.1%
8.53223 × 10121
0.1%
7.899426 × 10121
0.1%
7.5515 × 10121
0.1%
6.5724985 × 10121
0.1%

sp_ado_tfrt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct640
Distinct (%)99.1%
Missing95
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean35.11513591
Minimum0.283
Maximum112.9564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-23T17:30:48.904972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.283
5-th percentile2.08495
Q115.2069
median31.5562
Q351.27735
95-th percentile78.579
Maximum112.9564
Range112.6734
Interquartile range (IQR)36.07045

Descriptive statistics

Standard deviation24.68762142
Coefficient of variation (CV)0.7030478674
Kurtosis-0.1061679714
Mean35.11513591
Median Absolute Deviation (MAD)18.2223
Skewness0.6087423792
Sum22684.3778
Variance609.4786513
MonotonicityNot monotonic
2022-01-23T17:30:49.021573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.0036
 
0.8%
24.7592
 
0.3%
56.73961
 
0.1%
54.2981
 
0.1%
54.441
 
0.1%
54.5821
 
0.1%
55.12141
 
0.1%
55.66081
 
0.1%
56.20021
 
0.1%
57.2791
 
0.1%
Other values (630)630
85.0%
(Missing)95
 
12.8%
ValueCountFrequency (%)
0.2831
0.1%
0.2861
0.1%
0.28821
0.1%
0.29341
0.1%
0.29861
0.1%
0.30381
0.1%
0.3091
0.1%
0.3721
0.1%
0.4351
0.1%
0.4981
0.1%
ValueCountFrequency (%)
112.95641
0.1%
112.25381
0.1%
109.13541
0.1%
108.91321
0.1%
106.0171
0.1%
104.871
0.1%
103.63381
0.1%
101.67561
0.1%
101.25061
0.1%
98.86741
0.1%

sl_tlf_0714_zs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct33
Distinct (%)97.1%
Missing707
Missing (%)95.4%
Infinite0
Infinite (%)0.0%
Mean16.3464455
Minimum1.7
Maximum52.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-23T17:30:49.128595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile3.28
Q18.683533
median13.128205
Q318.334626
95-th percentile44.9528685
Maximum52.3
Range50.6
Interquartile range (IQR)9.651093

Descriptive statistics

Standard deviation12.93549114
Coefficient of variation (CV)0.7913335739
Kurtosis1.813258502
Mean16.3464455
Median Absolute Deviation (MAD)5
Skewness1.547009099
Sum555.779147
Variance167.326931
MonotonicityNot monotonic
2022-01-23T17:30:49.236244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
15.12
 
0.3%
42.827491
 
0.1%
22.021461
 
0.1%
12.41
 
0.1%
10.11
 
0.1%
161
 
0.1%
14.6990671
 
0.1%
40.61
 
0.1%
13.256411
 
0.1%
17.5385041
 
0.1%
Other values (23)23
 
3.1%
(Missing)707
95.4%
ValueCountFrequency (%)
1.71
0.1%
2.51
0.1%
3.71
0.1%
4.21
0.1%
5.0020841
0.1%
5.21
0.1%
6.61
0.1%
7.61
0.1%
8.61
0.1%
8.9341321
0.1%
ValueCountFrequency (%)
52.31
0.1%
48.91
0.1%
42.827491
0.1%
40.61
0.1%
34.51
0.1%
22.021461
0.1%
21.31
0.1%
19.91
0.1%
18.61
0.1%
17.5385041
0.1%

sp_dyn_le00_fe_in
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct616
Distinct (%)94.9%
Missing92
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean73.49832203
Minimum60.608
Maximum87.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-23T17:30:49.350997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum60.608
5-th percentile63.727
Q168.807
median72.816
Q378.301
95-th percentile84.92
Maximum87.7
Range27.092
Interquartile range (IQR)9.494

Descriptive statistics

Standard deviation6.242847333
Coefficient of variation (CV)0.08493863752
Kurtosis-0.6263126263
Mean73.49832203
Median Absolute Deviation (MAD)4.627
Skewness0.225662617
Sum47700.411
Variance38.97314283
MonotonicityNot monotonic
2022-01-23T17:30:49.471874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.24
 
0.5%
85.43
 
0.4%
85.53
 
0.4%
80.13
 
0.4%
84.63
 
0.4%
69.0432
 
0.3%
86.72
 
0.3%
84.82
 
0.3%
84.52
 
0.3%
68.6092
 
0.3%
Other values (606)623
84.1%
(Missing)92
 
12.4%
ValueCountFrequency (%)
60.6081
0.1%
60.6151
0.1%
60.6241
0.1%
60.7151
0.1%
60.911
0.1%
61.1161
0.1%
61.1241
0.1%
61.1951
0.1%
61.3621
0.1%
61.5251
0.1%
ValueCountFrequency (%)
87.71
0.1%
87.61
0.1%
87.32
0.3%
87.0511
0.1%
86.921
0.1%
86.91
0.1%
86.7811
0.1%
86.72
0.3%
86.6311
0.1%
86.4611
0.1%

sp_dyn_le00_ma_in
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct614
Distinct (%)94.6%
Missing92
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean68.85560247
Minimum56.186
Maximum82.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-23T17:30:49.589859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum56.186
5-th percentile60.0948
Q165.002
median68.361
Q372.9
95-th percentile79.36
Maximum82.3
Range26.114
Interquartile range (IQR)7.898

Descriptive statistics

Standard deviation5.636979806
Coefficient of variation (CV)0.08186668338
Kurtosis-0.5074160376
Mean68.85560247
Median Absolute Deviation (MAD)3.873
Skewness0.2152042343
Sum44687.286
Variance31.77554134
MonotonicityNot monotonic
2022-01-23T17:30:49.709028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.23
 
0.4%
72.13
 
0.4%
79.43
 
0.4%
78.52
 
0.3%
80.32
 
0.3%
80.12
 
0.3%
79.82
 
0.3%
73.92
 
0.3%
792
 
0.3%
78.42
 
0.3%
Other values (604)626
84.5%
(Missing)92
 
12.4%
ValueCountFrequency (%)
56.1861
0.1%
57.0011
0.1%
57.0441
0.1%
57.0781
0.1%
57.3621
0.1%
57.461
0.1%
57.5571
0.1%
57.6561
0.1%
57.9231
0.1%
57.9261
0.1%
ValueCountFrequency (%)
82.31
0.1%
81.91
0.1%
81.41
0.1%
81.31
0.1%
81.22
0.3%
81.1491
0.1%
81.11
0.1%
81.0131
0.1%
80.91
0.1%
80.8711
0.1%

Interactions

2022-01-23T17:30:45.941933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:38.988260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.885371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.670858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.558793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.442098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:43.378935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.337162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:45.078604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:46.131153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.107941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.980117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.766828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.660489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.555803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:43.483419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.423935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:45.185318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:46.220919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.202690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.074864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.857556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.751280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.645109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:43.575205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.507796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:45.275078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:46.308798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.295441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.158640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.947821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.849983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.738091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:43.741556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.595793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:45.363990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:46.402775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.395681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.246405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.040180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.952707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.842803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:43.835274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.669155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:45.459486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:46.506583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.502395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.335171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.215711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.060453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.957497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:43.942556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.753706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:45.565171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:46.603158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.602129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.419944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.300452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.156196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:43.062217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.063234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.830501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:45.659657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:46.683945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.681915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.503719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.384260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.235983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:43.158960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.142022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.910287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:45.740441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:46.781686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:39.782646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:40.587496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:41.472022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:42.339277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:43.271235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.240421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:44.995060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-23T17:30:45.845160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-23T17:30:49.811947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-23T17:30:49.959809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-23T17:30:50.103605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-23T17:30:50.328174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-23T17:30:46.940865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-23T17:30:47.124455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-23T17:30:47.281144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-23T17:30:47.410775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

economytimesi_pov_ginine_imp_gnfs_knbx_gsr_gnfs_cdny_gdp_mktp_cdsp_ado_tfrtsl_tlf_0714_zssp_dyn_le00_fe_insp_dyn_le00_ma_in
0ASM2000NaNNaNNaNNaNNaNNaNNaNNaN
1ASM2001NaNNaNNaNNaNNaNNaNNaNNaN
2ASM2002NaN1.053000e+09NaN512000000.0NaNNaNNaNNaN
3ASM2003NaN1.046000e+09NaN524000000.0NaNNaNNaNNaN
4ASM2004NaN9.110000e+08NaN509000000.0NaNNaNNaNNaN
5ASM2005NaN9.120000e+08NaN500000000.0NaNNaNNaNNaN
6ASM2006NaN8.780000e+08NaN493000000.0NaNNaNNaNNaN
7ASM2007NaN8.450000e+08NaN518000000.0NaNNaNNaNNaN
8ASM2008NaN8.630000e+08NaN560000000.0NaNNaNNaNNaN
9ASM2009NaN7.510000e+08NaN675000000.0NaNNaNNaNNaN

Last rows

economytimesi_pov_ginine_imp_gnfs_knbx_gsr_gnfs_cdny_gdp_mktp_cdsp_ado_tfrtsl_tlf_0714_zssp_dyn_le00_fe_insp_dyn_le00_ma_in
731WSM2009NaNNaN173604990.0584706050.030.3048NaN73.81969.231
732WSM2010NaNNaN204231280.0663155970.029.6202NaN74.03069.510
733WSM2011NaNNaN208397680.0737147970.028.9356NaN74.23369.782
734WSM2012NaNNaN222169070.0760319300.028.2510NaN74.42470.043
735WSM201338.7NaN230141460.0770059500.027.3780NaN74.60070.286
736WSM2014NaNNaN225924770.0756805950.026.5050NaN74.76270.511
737WSM2015NaNNaN231414420.0787958600.025.6320NaN74.91170.715
738WSM2016NaNNaN248249700.0799493900.024.7590NaN75.05270.895
739WSM2017NaNNaN278392000.0832025600.023.8860NaN75.18971.055
740WSM2018NaNNaN301045380.0821286900.023.2952NaN75.32571.197